Fine-grained Parallel Ant Colony System for Shared-Memory Architectures

Hadian, Ali; Shahrivari, Saeed; Minaei-bidgoli, Behrouz

doi:10.5120/8439-2223

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…In the fine-grained model, the population of ants is assigned to a large number of tiny groups, maintained by different processors. The information is exchanged frequently among those processors after each iteration [50]. The coarse-grained model is different.…”

Section: Solution Methodologymentioning

confidence: 99%

Distributed forecasting and ant colony optimization for the bike-sharing rebalancing problem with unserved demands

et al. 2019

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Bike-sharing systems (BSS) have widely spread over many cities in the world as an environmentally friendly means to reduce air pollution and traffic congestion. This paper focuses on the bike-sharing rebalancing problem (BRP), which consists of two aspects: determining desired demands at each station and designing routes to redistribute bikes among stations. For the first task, we firstly apply the random forest, a very efficient machine learning algorithm, to forecast desired demands for each station, which can be easily implemented with distributed computing. For the second task, it belongs to the broad class of the vehicle routing problem with pickup and delivery (VRPPD). In most existing settings, all of the demands being strictly satisfied can lead to longer routes and add operational costs. In this paper, we propose a new model with unserved demands by relaxing demands satisfying constraints. Then, we design a distributed ant colony optimization (ACO) based algorithm with some specific modifications to increase its efficiency for the proposed model. We propose to use the percentage of average cost saving per bike as a metric to evaluate the performance of our method on cost-reducing and compare with existing methods and best-known values. Computational results on benchmarks show the advantage of our approach. Finally, we provide a real case study of BSS in Hangzhou, China, with insightful elaborations.

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Section: Solution Methodologymentioning

confidence: 99%

Distributed forecasting and ant colony optimization for the bike-sharing rebalancing problem with unserved demands

et al. 2019

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“…Other solutions follow a divide-and-conquer approach (Doerner, Hartl, Benkner, & Luckà, 2006;Mocholi, Jaen, & Canos, 2005), based on the decomposition of the problem and the search for partial solutions in different processes while a coordinator builds the complete solution afterwards. Shared memory implementations are also proposed in Delisle, Gravel, Krajecki, Gagné, and Price (2005), Delisle et al (2001) and Hadian, Shahrivari, and Minaei-Bidgoli (2012), where small tasks are distributed among processes while a coordinator is in charge of carrying out the update of the global information and control the progress of the algorithm.…”

Section: Related Workmentioning

confidence: 99%

Parallel ant colony optimization for the training of cell signaling networks

González

Gábor²,

Sáez-Rodríguez³

et al. 2022

Expert Systems with Applications

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Parallel computational optimization in operations research: A new integrative framework, literature review and research directions

Schryen

2020

European Journal of Operational Research

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Solving optimization problems with parallel algorithms has a long tradition in OR. Its future relevance for solving hard optimization problems in many fields, including finance, logistics, production and design, is leveraged through the increasing availability of powerful computing capabilities. Acknowledging the existence of several literature reviews on parallel optimization, we did not find reviews that cover the most recent literature on the parallelization of both exact and (meta)heuristic methods. However, in the past decade substantial advancements in parallel computing capabilities have been achieved and used by OR scholars so that an overview of modern parallel optimization in OR that accounts for these advancements is beneficial. Another issue from previous reviews results from their adoption of different foci so that concepts used to describe and structure prior literature differ. This heterogeneity is accompanied by a lack of unifying frameworks for parallel optimization across methodologies, application fields and problems, and it has finally led to an overall fragmented picture of what has been achieved and still needs to be done in parallel optimization in OR. This review addresses the aforementioned issues with three contributions: First, we suggest a new integrative framework of parallel computational optimization across optimization problems, algorithms and application domains. The framework integrates the perspectives of algorithmic design and computational implementation of parallel optimization. Second, we apply the framework to synthesize prior research on parallel optimization in OR, focusing on computational studies published in the period 2008-2017. Finally, we suggest research directions for parallel optimization in OR.Keywords computing science · parallel optimization · computational optimization · literature review * I am grateful for the support provided by Abdullah Burak, Philip Empl, Constanze Hilmer, Gerhard Rauchecker, Richard Schuster, Henning Siemes, and Melih Yilmaz, who supported me substantially in searching and coding research articles.2 Impressive computational results of applying parallelization to the traveling salesman problem (TSP) are reported by Crainic et al. [2006, p.2]. arXiv:1910.03028v1 [cs.DC] 3 Oct 2019Parallel computational optimization in operations research is challenging in general from both the algorithmic and the computational perspective, and ii) a viable alternative to parallelizing algorithms has been the exploitation of ongoing increases of clock speed of single CPUs of modern microprocessors. But this growth process reached a limit already several years ago due to heat dissipation and energy consumption issues . This development makes parallelization efforts (not only in optimization) much more important than it was in earlier times.Fortunately, the need for parallelization has been acknowledged and accompanied by an increased availability of parallel computing resources. This availability is rooted in two phenomena: a) the rapid development of para...

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Fine-grained Parallel Ant Colony System for Shared-Memory Architectures

Cited by 3 publications

References 29 publications

Distributed forecasting and ant colony optimization for the bike-sharing rebalancing problem with unserved demands

Distributed forecasting and ant colony optimization for the bike-sharing rebalancing problem with unserved demands

Parallel ant colony optimization for the training of cell signaling networks

Parallel computational optimization in operations research: A new integrative framework, literature review and research directions

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